Analysis of Training Loss and Validation Loss Graph
Simply model 1 is a better fit compared to model 2.


Graph for model 1

We notice that the training loss and validation loss aren't correlated. This means the as the training loss is decreasing, the validation loss remains the same of increases over the iterations. This means that the model is not exactly improving, but is instead overfitting the training data. This isn't what we are looking for.
Graph for model 2

In this case, there is clearly a health correlation between training loss and the validation loss. They both seem to reduce and stay at a constant value. This means that the model is well trained and is equally good on the training data as well as the hidden data.


You should stick with model 2. In case you're going ahead with model 1, make sure to use the chechpoint where both the losses are at a similar value (at around 100 -150 epochs)